Patients’ preferences in transplantation from marginal donors: results of a discrete choice experiment
Bibliographic record
Abstract
To increase the donor pool, the strategy of transplantation from "marginal" donors was developed though patients' preferences about these donors were insufficiently known. The preferences of patients registered on the waiting list or already transplanted in eight transplant teams covering four main organs (i.e., kidney, liver, heart, and lung) were evaluated using the discrete choice experiment method. In each left during 2 days, patients were interviewed on four scenarios. Of 178 eligible patients, 167 were interviewed; 40% accepted marginal graft in their own situation and 89% at least in one of the scenarios. Imagining urgent situations or rare profiles with difficult access to transplantation, respectively, 86% and 71% accepted these grafts. Most (76%) preferred to be informed about these grafts and 43% preferred to be involved in decision. The emergency [OR = 1.24; 95% CI: (1.06-1.45)] and the hazardousness [OR = 0.88; 95% CI: (0.78-0.99)] of the transplantation were factors independently associated with marginal graft acceptance. Most patients preferred to be informed and to be involved in the decision. Marginal grafts could be more accepted by patients in critical medical situations or perceiving their situation as critical. Physicians' practices in transplantation should be reconsidered taking into account individual preferences. This study was performed in a single country and thus reflects the cultural bias and practice thereof.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".